期刊名： Machine Learning and Knowledge Extraction

期刊主页：https://www.mdpi.com/journal/make

1. 期刊介绍

Machine Learning and Knowledge Extraction (MAKE) (ISSN 2504-4990) 是一个国际性的、经过同行评审的开放获取期刊（读者免费），致力于发表机器学习与知识提取所有领域相关的原创研究论文、综述文章、通讯和观点。期刊编委由领域内资深学者组成，负责把控所发表文章的质量以及期刊的整体发展方向。该期刊为计算机科学、数据科学、医学、工程、教育等不同领域但共同关注智能数据分析和知识发现的研究人员提供了一个跨学科发表平台。

2026年9月，MDPI期刊Machine Learning and Knowledge Extraction正式被Ei Compendex 收录。在此，衷心感谢作者对期刊的支持，感谢主编、编委、客座编辑与审稿专家的重要贡献和读者的持续关注。

期刊主编

Andreas Holzinger教授

格拉茨工业大学

主编寄语

机器学习旨在理解智能，以设计能够从数据中学习、从经验中获取知识并随时间不断改进学习行为的算法。其挑战在于从数据中提取相关的结构和/或时间模式（即“知识”），而这些模式往往隐藏在高维空间中，人类难以直接触及。许多应用领域，例如智慧医疗、智慧工厂等，正深刻影响着我们的日常生活，例如推荐系统、语音识别、自动驾驶等。而重大挑战在于，如何在不确定性下理解真实世界的上下文。概率推理在此可以发挥巨大作用，因为逆概率允许我们从数据中学习、推断未知，并做出预测以支持决策。

文章推荐

1. Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models

推进医学影像中的人工智能可解释性：像素级可解释性与 Grad-CAM 模型的比较分析

Mohammad Ennab and Hamid Mcheick

https://www.mdpi.com/2504-4990/7/1/12

Ennab, M.; Mcheick, H. Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models. Mach. Learn. Knowl. Extr. 2025, 7, 12.

2. Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning

客户流失预测：机器学习和深度学习近期进展、趋势与挑战的系统综述

Mehdi Imani, Majid Joudaki, Ali Beikmohammadi, and Hamid Reza Arabnia.

https://www.mdpi.com/2504-4990/7/3/105

Imani, M.; Joudaki, M.; Beikmohammadi, A.; Arabnia, H.R. Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning. Mach. Learn. Knowl. Extr. 2025, 7, 105.

3. Exploring the Intersection of Machine Learning and Big Data: A Survey

探索机器学习与大数据交叉的综述

Elias Dritsas and Maria Trigka.

https://www.mdpi.com/2504-4990/7/1/13

Dritsas, E.; Trigka, M. Exploring the Intersection of Machine Learning and Big Data: A Survey. Mach. Learn. Knowl. Extr. 2025, 7, 13.

4. Machine Learning for Nanomaterial Discovery and Design

机器学习用于纳米材料发现与设计

Antonio del Bosque, Pablo Fernández-Arias, and Diego Vergara.

https://www.mdpi.com/2504-4990/8/1/10

del Bosque, A.; Fernández-Arias, P.; Vergara, D. Machine Learning for Nanomaterial Discovery and Design. Mach. Learn. Knowl. Extr. 2026, 8, 10.

5. Artificial Intelligence Models for Forecasting Mosquito-Borne Viral Diseases in Human Populations: A Global Systematic Review and Comparative Performance Analysis

用于预测人类群体中蚊媒病毒性疾病的人工智能模型：全球系统综述与比较性能分析

Flavia Pennisi, Antonio Pinto, Fabio Borgonovo, Giovanni Scaglione, Riccardo Ligresti, Omar Enzo Santangelo, Sandro Provenzano, Andrea Gori, Vincenzo Baldo, Carlo Signorelli, and et al.

https://www.mdpi.com/2504-4990/8/1/15

Pennisi, F.; Pinto, A.; Borgonovo, F.; Scaglione, G.; Ligresti, R.; Santangelo, O.E.; Provenzano, S.; Gori, A.; Baldo, V.; Signorelli, C.; et al. Artificial Intelligence Models for Forecasting Mosquito-Borne Viral Diseases in Human Populations: A Global Systematic Review and Comparative Performance Analysis. Mach. Learn. Knowl. Extr. 2026, 8, 15.

精选特刊

Trustworthy AI: Integrating Knowledge, Retrieval, and Reasoning

投稿截止日期: 2027年1月31日

客座编辑： Prof. Dr. Konstantinos Diamantaras

https://www.mdpi.com/journal/make/special_issues/4UK08IG62O

Clinically Robust and Transparent AI-Assisted Medical Diagnostics: From Learning Dynamics to Real-World Deployment

投稿截止日期: 2027年4月30日

客座编辑：Dr. Milan Toma and Prof. Dr. Constantinos Pattichis

https://www.mdpi.com/journal/make/special_issues/UEMX8L5R2Z